The present invention relates to synthesis of medical image data, and more particularly, to synthesizing subject-specific medical image data across image domains or image modalities.
A multitude of imaging modalities, such as X-ray, computed tomography (CT), diffuser tensor imaging (DTI), T1-weighted magnetic resonance imaging (MRI), T2-weighted MRI, etc., can be used for medical image analysis of a of a patient. Each of these imaging modalities captures different characteristics of the underlying anatomy and the relationship between any two modalities is highly nonlinear.
In many practical medical image analysis problems, a situation is often encountered in which medical image data available for training, for example for machine learning based anatomical object detection, has a different distribution or representation than the medical image data given during testing due to modality heterogeneity or domain variation. Due to variations in the image characteristics across modalities, medical image analyses algorithms trained with data from one modality may not work well when applied to medical image data from a different modality. A straightforward way to address this issue is to collect large amounts of training data from each imaging modality. However, this solution is impractical since collecting medical images is often time consuming and expensive.
Cross-modal synthesis generates medical images in a desired target modality from given source modality images. The ability to synthesize medical images without actual acquisition has many potential applications, such as atlas construction, multi-modal registration, super-resolution, and building virtual models. Various approaches for cross-modal synthesis have been proposed, but such approaches are either tailored to specific applications or work under a supervised setting in which training data from the same set of subjects in both the source and target modalities is required. Availability of such paired data is often limited and collecting such paired data is not desirable because each subject must be scanned multiple times. Accordingly, an unsupervised cross-modal medical image synthesis approach that generates target modality images without the need for paired training data is desirable.